The main purpose of this paper is to investigate the volatility in the cryptocurrency market and the relationships between them, using the cryptocurrencies: Bitcoin, Cardano and Stellar. The results obtained suggest that volatility in cryptocurrency prices is influenced by previous events and the level of past volatility. The propagation of volatility and shocks between the three analyzed cryptocurrencies, and the observation of the interconnection between them was also followed in this paper. By determining the degree of correlation between the analyzed cryptocurrencies, we observed that in the selected period there are quite strong positive correlations. Also, we noticed that the volatility of cryptocurrencies was strongly influenced by certain economically uncertain periods, a fact that caused their prices to have a strong fluctuation, especially in the period 2021-2023. The results obtained show an interdependence between the three cryptocurrencies, which is significant in the decision-making of investors. News and events play a significant role in the cryptocurrency market, especially those in the financial, technological, and political worlds that can have a considerable impact on cryptocurrency prices and volatility. In the case of Bitcoin, there was an increase in interest in this cryptocurrency when there were investments from large companies and financial institutions.
References
[1]
Anghel, L. C., Zwak-Cantoriu, M. C, Mendon, S., Gyorgy, A., Ermiș, S., & Trivedi, J. (2022). Financial Market Interconnections Analyzed Using Garch Univariate and Multivariate Models. Journal of Economic Computation and Economic Cybernetics Studies and Research, 56, 101-118. https://doi.org/10.24818/18423264/56.3.22.07
[2]
Baba, Y., Engle, R. F., Kraft, D., & Kroner, K. F. (1985). Multivariate Simultaneous Generalized ARCH. Unpublished Paper, Department of Economics, University of California. [Published as Engle and Kroner (1995)]
[3]
Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating Volatility Transmission and Hedging Properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219-227. https://doi.org/10.1016/j.ribaf.2019.01.001
[4]
Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model. The Review of Economics and Statistics, 72, 498-505. https://doi.org/10.2307/2109358
[5]
Clements, A. E., Hurn, A. S., & Volkov, V. V. (2015). Volatility Transmission in Global Financial Markets. Journal of Empirical Finance, 32, 3-18. https://doi.org/10.1016/j.jempfin.2014.12.002
[6]
Corbet, S., Larkin, B., Lucey, A., Meegan, A., & Yarovaya, L. (2020). Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position. Journal of Financial Stability, 46, Article 100706. https://doi.org/10.1016/j.jfs.2019.100706
[7]
Corbet, S., Larkin, B., Lucey, A., Urquhart, A., & Yarovaya, L. (2019). Cryptocurrencies as a Financial Asset: A Systematic Analysis. International Review of Financial Analysis, 62, 182-199. https://doi.org/10.1016/j.irfa.2018.09.003
[8]
Engle, R. F., & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11, 122-150. https://doi.org/10.1017/S0266466600009063
[9]
Fuller, W. A. (1976). Introduction to Statistical Time Series (2nd ed.). John Wiley and Sons.
[10]
Galariotis, E. C., Wu, R., & Spyrou, S. I. (2015). Herding on Fundamental Information: A Comparative Study. Journal of Banking & Finance, 50, 589-598. https://doi.org/10.1016/j.jbankfin.2014.03.014
[11]
Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility Spillover Effects in Leading Cryptocurrencies: A BEKK-MGARCH Analysis. Finance Research Letters, 29, 68-74. https://doi.org/10.1016/j.frl.2019.03.009
[12]
Makarov, I., & Schoar, A. (2022). Cryptocurrencies and Decentralized Finance (DeFi). NBER. https://doi.org/10.3386/w30006
[13]
Ngunyi, A., Mundia, S., & Omari, C. (2019). Modeling Volatility Dynamics of Cryptocurrencies Using GARCH Models. Journal of Mathematical Finance, 9, 591-615. https://doi.org/10.4236/jmf.2019.94030
[14]
Panagiotidis, T., Papapanagiotou, G., & Stengos, T. (2022). On the Volatility of Cryptocurrencies. Research in International Business and Finance, 62, Article 101724. https://doi.org/10.1016/j.ribaf.2022.101724
[15]
Pearson, K. (1985). Notes on Regression and Inheritance in the Case of Two Parents. Proceedings of the Royal Society of London, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041
[16]
Phillips, P. C. B., & Perron, P. (1988) Testing for a Unit Root in Time Series Regression. Biometrika, 75, 335-346. https://doi.org/10.1093/biomet/75.2.335
[17]
Sakthivel, P., Bodkhe, N., & Kamaiah, B. (2012). Correlation and Volatility Transmission across International Stock Markets: A Bivariate GARCH Analysis. International Journal of Economics and Finance, 4, 253-264. https://doi.org/10.5539/ijef.v4n3p253
[18]
Thies, S., & Molnár, P. (2018). Bayesian Change Point Analysis of Bitcoin. Finance Research Letters, 27, 223-227. https://doi.org/10.1016/j.frl.2018.03.018
[19]
Wang, P., Liu, X., Wu, S. (2022). Dynamic Linkage between Bitcoin and Traditional Financial Assets: A Comparative Analysis of Different Time Frequencies. Entropy, 24, Article 1565. https://doi.org/10.3390/e24111565
[20]
Yousaf, I., & Yarovaya, L. (2022). Herding Behavior in Conventional Cryptocurrency Market, Non-Fungible Tokens, and DeFi Assets. Finance Research Letters, 50, Article 103299. https://doi.org/10.1016/j.frl.2022.103299
